




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Any						=     LongformerTokenizer
				__a     :       Any						=     True
				__a     :       List[Any]						=     LongformerTokenizerFast
				__a     :       Any						=     True






				def 		snake_case	(			self ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							SCREAMING_SNAKE_CASE_   : Dict					  =      [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dict(zip(snake_case__      ,range(len(snake_case__ ) ) ) )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'unk_token': '<unk>'}

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file'] )
							SCREAMING_SNAKE_CASE_   : int					  =      os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file      ,'w'      ,encoding='utf-8' ) as fp:
										fp.write(json.dumps(snake_case__ ) + '\n' )
							with open(self.merges_file      ,'w'      ,encoding='utf-8' ) as fp:
										fp.write('\n'.join(snake_case__ ) )






				def 		snake_case	(			self      ,**snake_case__ ):
							kwargs.update(self.special_tokens_map )
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**snake_case__ )






				def 		snake_case	(			self      ,**snake_case__ ):
							kwargs.update(self.special_tokens_map )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'lower newer'
							SCREAMING_SNAKE_CASE_   : Any					  =      'lower newer'
							return input_text, output_text






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.tokenizer_class(self.vocab_file      ,self.merges_file      ,**self.special_tokens_map )
							SCREAMING_SNAKE_CASE_   : Any					  =      'lower newer'
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							SCREAMING_SNAKE_CASE_   : int					  =      tokenizer.tokenize(snake_case__ )  # , add_prefix_space=True)
							self.assertListEqual(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      tokens + [tokenizer.unk_token]
							SCREAMING_SNAKE_CASE_   : Tuple					  =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('Hello world!'      ,add_special_tokens=snake_case__ )      ,[0, 31414, 232, 328, 2] )
							self.assertListEqual(
							    tokenizer.encode('Hello world! cécé herlolip 418'      ,add_special_tokens=snake_case__ )      ,[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]      ,)






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      tokenizer.encode('sequence builders'      ,add_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer.encode('multi-sequence build'      ,add_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.encode(
							    'sequence builders'      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer.encode(
							    'sequence builders'      ,'multi-sequence build'      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.build_inputs_with_special_tokens(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer.build_inputs_with_special_tokens(snake_case__      ,snake_case__ )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Dict					  =      'Encode this sequence.'
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.byte_encoder[' '.encode('utf-8' )[0]]

							# Testing encoder arguments
							SCREAMING_SNAKE_CASE_   : int					  =      tokenizer.encode(snake_case__      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
							self.assertNotEqual(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer.encode(snake_case__      ,add_special_tokens=snake_case__      ,add_prefix_space=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
							self.assertEqual(snake_case__      ,snake_case__ )

							tokenizer.add_special_tokens({'bos_token': '<s>'} )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer.encode(snake_case__      ,add_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.convert_ids_to_tokens(encoded[1] )[0]
							self.assertNotEqual(snake_case__      ,snake_case__ )

							# Testing spaces after special tokens
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      '<mask>'
							tokenizer.add_special_tokens(
							    {'mask_token': AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ )} )  # mask token has a left space
							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.convert_tokens_to_ids(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'Encode <mask> sequence'
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'Encode <mask>sequence'

							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.encode(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      encoded.index(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
							self.assertEqual(snake_case__      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer.encode(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      encoded.index(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
							self.assertNotEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							pass






				def 		snake_case	(			self ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.rust_tokenizer_class.from_pretrained(snake_case__      ,**snake_case__ )
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.tokenizer_class.from_pretrained(snake_case__      ,**snake_case__ )
													SCREAMING_SNAKE_CASE_   : Dict					  =      'A, <mask> AllenNLP sentence.'
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer_r.encode_plus(snake_case__      ,add_special_tokens=snake_case__      ,return_token_type_ids=snake_case__ )
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer_p.encode_plus(snake_case__      ,add_special_tokens=snake_case__      ,return_token_type_ids=snake_case__ )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['token_type_ids'] )      ,sum(tokens_p['token_type_ids'] ) )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )      ,sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )      ,)

													SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['input_ids']      ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
													self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

													self.assertSequenceEqual(
													    snake_case__      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
													self.assertSequenceEqual(
													    snake_case__      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )






				def 		snake_case	(			self ):
							for trim_offsets, add_prefix_space in itertools.product([True, False]      ,repeat=2 ):
										SCREAMING_SNAKE_CASE_   : int					  =      self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )

										SCREAMING_SNAKE_CASE_   : List[str]					  =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

										self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,snake_case__ )

										self.assertEqual(post_processor_state['add_prefix_space']      ,snake_case__ )
										self.assertEqual(post_processor_state['trim_offsets']      ,snake_case__ )






				def 		snake_case	(			self ):
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
													SCREAMING_SNAKE_CASE_   : List[str]					  =      'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
													SCREAMING_SNAKE_CASE_   : Dict					  =      F'{text_of_1_token} {text_of_1_token}'

													SCREAMING_SNAKE_CASE_   : Tuple					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : str					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ) + 1, len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Any					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ) + 1, len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Dict					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : str					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ), len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : int					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(len(snake_case__ ), len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : List[Any]					  =      F' {text}'

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													SCREAMING_SNAKE_CASE_   : Any					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : Tuple					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(1, 1 + len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(1 + len(snake_case__ ) + 1, 1 + len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, 1 + len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(1 + len(snake_case__ ), 1 + len(snake_case__ ) + 1 + len(snake_case__ ))      ,)

													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.rust_tokenizer_class.from_pretrained(
													    snake_case__      ,use_fast=snake_case__      ,add_prefix_space=snake_case__      ,trim_offsets=snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer_r(snake_case__      ,return_offsets_mapping=snake_case__      ,add_special_tokens=snake_case__ )
													self.assertEqual(encoding.offset_mapping[0]      ,(0, 1 + len(snake_case__ )) )
													self.assertEqual(
													    encoding.offset_mapping[1]      ,(1 + len(snake_case__ ), 1 + len(snake_case__ ) + 1 + len(snake_case__ ))      ,)








'''simple docstring'''



from math import pow
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		):
							if current_sum == needed_sum:
														# If the sum of the powers is equal to needed_sum, then we have a solution.
														solutions_count += 1
														return current_sum, solutions_count

							SCREAMING_SNAKE_CASE       :   Tuple         =      int(pow(A       ,		A					)					)
							if current_sum + i_to_n <= needed_sum:
														# If the sum of the powers is less than needed_sum, then continue adding powers.
														current_sum += i_to_n
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Dict         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
														current_sum -= i_to_n
							if i_to_n < needed_sum:
														# If the power of i is less than needed_sum, then try with the next power.
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
							return current_sum, solutions_count
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
														raise ValueError(
														    '''Invalid input\n'''
														    '''needed_sum must be between 1 and 1000, power between 2 and 10.'''					)

							return backtrack(A       ,		A       ,		1       ,		0       ,		0					)[1]  # Return the solutions_count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




